Many customs administrations have a mandate to be recognized as a world-class border service respected for their professionalism, efficiency, fairness, and contribution towards sovereign economic and social development.
Across the world, international best practices for Customs have evolved from standards and agreements over the last several years such as the World Customs Organization (WCO) Framework of Standards to Facilitate and Secure Global Trade (SAFE) and the WCO’s Columbus Program for Customs Modernization and Capacity Building. By conforming to these existing standards a customs service can promote and foster integrity, responsible enforcement, and good governance within its Customs programs.
A key component of best practices for Customs modernization is effective risk management. Risk management has emerged as the guiding principle for border management to allow the focusing of resources on high risk shipments while promoting pre-approved and/or low risk trade.
In line with the WCO SAFE framework, modern Customs administrations have begun adopting the use of automated risk management systems to improve their ability to identify high risk shipments and promote trade facilitation. These systems have evolved to become the core of the Customs risk management regime in those countries. Performance is measured by increased enforcement, enhanced security, more trade, and a surge in the movement of transshipments resulting in increased revenue, and economic and social development.
Border control agencies, including customs authorities, face major challenges balancing a country’s need for controls with the benefits of facilitating cross border traffic in people and goods. To better manage the growing volume of travelers and trade, a significant number of leading customs administrations have adopted risk management as the guiding principle for border management. Systematically Implementing risk management at strategic, operational, and tactical levels ensures that customs administrations best deploy resources to protect their citizens from threats to health, safety, and security, while simultaneously supporting economic growth by maintaining efficient and predictable cross border transit times.
Many developing Customs services currently use risk adverse approaches requiring a full inspection of many/all shipments, conveyances, crews and passengers. This “gatekeeper” approach has the following shortcomings:
- Costly in resources as it applies the same degree of intensity to all threats
- Constrained in that it forces a lower degree of inspection intensity overall due to a uniform treatment of all cargo and passengers
- Creates a high incidence of officer errors due to higher workloads
- Realizes fewer enforcement results
- Encourages normally law-adhering entities to circumvent the system to hasten the cross-border transit of their goods
- Creates opportunities for criminals to circumvent and avoid interdiction by making customs reactions predictable
- Slows the supply chain
- Hinders economic growth
- Does not scale
- Fails ultimately to achieve efficient, secure border management
In moving away from such draconian risk adverse approaches, many large customs administrations have demonstrated that technology is a key efficiency enabler. The WCO SAFE Framework of Standards to Secure and Facilitate Global Trade specifically mentions that customs administrations should develop or procure automated risk management systems. Based on international best practices, risk assessment systems use standard data sets and strategic intelligence to support identification of high-risk shipments and travelers.
Adopting an automated risk assessment system is a significant step towards successfully adopting risk management practices strategically, operationally, and tactically. Border control processes that use risk assessment systems help ensure that customs resources are always focused on the highest risk shipments and people in real time.
Automated targeting and risk assessment systems process electronic pre-arrival data in an analytical workflow to identify the shipments or passengers of most interest. Low risk shipments and passengers are facilitated and high risk ones are set aside for additional scrutiny, up to and including physical inspections or orders not to load cargo or fly passengers. When combined with random inspections, pre-arrival data can significantly augment threat identification and interdiction, thus improving an administration’s effectiveness in meeting national economic and security mandates.
Successful implementation of risk assessment systems requires the mandated use of electronically submitted pre-arrival data. Many customs administrations use real time pre-arrival data for a variety of purposes, including:
- Increasing cargo control
- Recouping lost or evaded revenue
- Gathering intelligence
- Profiling risks
- Setting lookouts
- Identifying potential security threats
- Enhancing end-to-end supply chain or travel visibility in order to identify anomalous behaviour in relation to people, entities, commodities, and routings
Pre-arrival data provides customs administrations with the ability to “push the border out” virtually and decide whether to facilitate or intercept cargo or people before they reach the physical border.
GreenLine Systems recommends adopting risk management as the core foundation to any customs or border modernization activity, with due attention paid to the following specific tasks:
- Update customs and trade legislation, regulations, standard operating procedures, and directives to provide authority to field personnel to adopt and use risk based decisions for all travelers and trade entering or leaving the country.
- Adopt a simple, clear, and unified framework and approach to risk management at strategic, tactical, and operational levels.
- Perform a detailed analysis and ranking of threats.
- Acquire human and technical resources to enable:
- Separation of duties to enhance integrity
- Collection of pre-arrival data
- Implementation of trusted trader and registered traveler programs
- Expansion of available inspection types to allow for varying intensities of inspections
- Establishment of control processes throughout the pre-arrival, arrival and post-clearance stages of the customs process
- Capture of all outcomes in automated systems
- Establishment of a post-clearance audit and analysis unit to continually validate, monitor and review the above
Adopting border risk management at strategic levels within an administration is the first step. Transitioning to risk managed decision making at all levels requires a multi-faceted organizational change involving new policy, clear and continuous communication, technology, and training. Above all, it requires persistence. Technology adoption is often the most significant catalyst for establishing a risk management framework within any customs or border organization. Acquiring technology provides momentum for change and allows an administration to modernize to the most current and leading edge solutions while avoiding many development hurdles born by other customs administrations. Using systems already adopted by other administrations can cut time and cost by leveraging the best practices and benchmarks already incorporated in tested technology.
Historical Data Mining and Operational Decision Support
One of the key capabilities required from custom’s risk management is risk profiling and targeting. As a result, data mining techniques are used regularly to identify risk profiles. Data mining often uses machine learning and statistical methods to classify and group the high risk profiles and target groups. Though, it has scientific roots, it is based on a few assumptions which can be flawed if not clearly understood.
It is important to understand that data mining is not a front end selectivity function used by end users such as customs officers. Generally, data mining techniques (historical trend analysis, forecasting, business intelligence, entity hop/link charting, etc.) are used to identify risk profiles by intelligence analysts working in a strategic intelligence environment. These profiles often are turned into risk indicator rules in front end decision support systems (targeting and risk assessment systems) for selectivity decisions by customs officers. Selectivity decisions eliminate low risk shipments for trade facilitation (which in turn promotes economic growth) and scrutinize, inspect, or intercept high risk shipments that may pose a significant threat to a nation or region of the globe.
Customs Organizations are generally “Data Rich”, but “Knowledge Poor”
While customs administrations collect regulatory filings from carriers (e.g. Cargo declarations, manifests, etc.) and importers (importer declarations), the growing volume of this information has become difficult to manage. While the volume of global trade continues to grow at the rate of 10% each year, the data expands at the same rate. As such, customs administrations should be anticipating data volumes to double every 10 years. Using this data effectively in an operational environment for selectivity decisions to determine highest risk shipments is key, yet many vendors in the market place today will promote back-end historical trend analysis tools using business intelligence and data mining techniques to identify anomalous behavior in the data that may suggest nefarious activity. While important, this approach does not transfer to an operational environment where a decision support system is paramount. Data mining identifies profiles based on strategic analysis of large data sets. Whereas, targeting and selectivity solutions use risk indicator rules (based on these profiles) in a front end decision support system.
The following diagram attempts to clarify the difference between 1) data mining and 2) operational decision support:
Profiles, Statistics, Strategic management reports
Intel Analyst, Statisticians, Managers
Historical (large datasets – eg. 5-7 years )
SAS, SPSS, Clementine, Semantic, I2, Detica
Back end historical trend analysis
Tactical and Operational
Risk Indicator Rules
Customs Officers, Enforcement teams, Interdiction officers, Inspection teams
Current and Real Time (smaller windows of data – eg. 90 day operational database containing most recent information on file)
GreenLine’s Cargo Solution for Customs Risk Management, MDA WatchKeeper, ATS, TITAN, etc.
Targeting and Selectivity – Mission Critical – Front end decision support (decisions required to be made in real time)
While we acknowledge the importance of data mining as part of an overall risk management solution, we need to address the operational real-time requirements and tactical needs of the customs operation. We propose the use of a comprehensive rule sets that have been validated by custom experts.
As previously mentioned, international best practices for customs have evolved from standards and agreements such as the Revised Kyoto Convention on the Simplification and Harmonization of Customs Procedures, the World Customs Organizations (WCO) Framework of Standards to Facilitate and Secure Global Trade (SAFE), and the WCO’s Columbus Program for Customs Modernization and Capacity Building.
A key component of international best practices for customs modernization, risk management has emerged as the guiding principle for border management to allow the focusing of resources on high risk shipments while promoting pre-approved and/or low risk trade. In line with the WCO SAFE framework, the majority of customs administrators have already adopted the principles of risk management.
In line with this, Modern Customs have begun adopting the use of automated risk management systems to improve their ability to identify high risk shipments and promote trade facilitation. In fact, the use of these systems has evolved to become the core of the customs risk management regime in these countries, with performance measured by increased enforcement, enhanced security, more trade, and a surge in the movement of transshipments resulting in increased revenue, and economic and social development.
The GreenLine Cargo Solution for Customs Risk Management (GCScrm™) will automatically risk assess all manifest and importer data when filed by the appropriate trade chain partner and present the associated shipments in order of risk for review by the field/customs officers. In addition to the profiles established through the analysis of post-clearance historical data, the system will be augmented with our WCO Risk Assessment Modules (RAMs). These 5 RAMs have been developed and based on the WCO SAFE Framework and WCO’s Standardized Risk Assessments (SRAs) and Global High Risk Indicator Document (GHRI). These RAMs are designed to trigger and identify on the risks associated with the WCO SRAs which include:
- Revenue Evasion
The GCScrm tiers the manifest and declaration data in order of high, medium, and low risk scores. This will facilitate the triage of the data by an officer (manually of though an automated threshold score established by Customs). Presenting the data in this manner allows a Customs Service to ensure its officers are focused on the highest risk trade while pre-approved and/or low risk trade can be facilitated. The officer has the ability to work the data manually through an analytical workflow and refer high risk shipments for inspection to examination teams or other customs officers. Results are collected in an inspection template and held on file for historical query and future analytical usage.
This approach has been proven to be highly successful in identifying and interdicting threats upon arrival at a customs port of entry. Data mining and real-time decision support go hand in hand in a customs environment.
Additional Detail and content can be provided upon request by contacting Chris Thibedeau, VP WW Customs, Regulatory and Law Enforcement Solutions, GreenLine Systems. Ph: 011-613-884-8162 or email: firstname.lastname@example.org